L14 Tables 1

Data Visualization (STAT 302)

Author

INSTRUCTIONS

Overview

The goal of this lab is to explore static tables using the grammar of tables from the gt and gtExtras package.

The gt() function creates a gt table object when provided with table data. Using this function is the first step in a typical gt workflow. Once we have the gt table object, we can perform styling transformations by adding on column, formatting, tab styling, and summary layers.

A few helpful links to provide more information about the gt package: gt rstudio and intro to gt

Posit (formally RStudio) hosts an annual table competition! Check out some past winning tables: 2021 2022

Dataset

We will be using the pizzaplace dataset included in the gt package, the house_prices, stream_data, and team_logo dataset which should be located in your /data subdirectory. Each dataset will require a bit of data wrangling provided in their respective exercise code chunk.

Exercise 1

Use the pizza_summary dataset created below to recreate the following graphic as precisely as possible. We will work through this exercise together to provide a high level overview of a few of the ways you can layer on table elements to make a simple but visually appealing table.

Hints:

  • md(): Interpret input text as Markdown-formatted text
Code 
# data wrangling
pizza_summary <- pizzaplace %>% 
  filter(type %in% c("classic", "chicken")) %>% 
  group_by(name, type) %>% 
  summarize(
    number_sold = n(),
    profit = sum(price)
    ) %>% 
  ungroup()
Pizza Place Annual Pizza Sales
Fiscal Year 2015
Number
Sold		 Annual
Revenue
classic
big_meat 1,914 $22,968.00
classic_dlx* 2,453 $38,180.50
hawaiian 2,422 $32,273.25
ital_cpcllo 1,438 $25,094.00
napolitana 1,464 $24,087.00
pep_msh_pep 1,359 $18,834.50
pepperoni 2,418 $30,161.75
the_greek 1,420 $28,454.10
chicken
bbq_ckn 2,432 $42,768.00
cali_ckn 2,370 $41,409.50
ckn_alfredo 987 $16,900.25
ckn_pesto 973 $16,701.75
southw_ckn 1,917 $34,705.75
thai_ckn 2,371 $43,434.25
Source: gt package dataset
* Our most popular pizza!

Exercise 2

Using the pizza_size dataset derived from the pizzaplace dataset, recreate the following graphic as precisely as possible.

Code 
# data wrangling
pizza_size <- pizzaplace %>% 
  filter(
    size %in% c("S", "L", "M"), 
    type %in% c("chicken", "classic", "veggie")
    ) %>% 
  group_by(type, size) %>% 
  summarize(
    number_sold = n(),
    profit = sum(price)
    ) %>% 
  ungroup() %>% 
  rename(category = type)

Table 1

Hints:

  • tab_style() is useful for changing cell colors
  • Hex codes: "#F8766D", "#00BA38", "#619CFF"
  • size L: bold
  • size M: italic
  • size S: "white"
Pizza Place Annual Pizza Sales
Fiscal Year 2015
number_sold profit
chicken
L 4932 102339.00
M 3894 65224.50
S 2224 28356.00
classic
L 4057 74518.50
M 4112 60581.75
S 6139 69870.25
veggie
L 5403 104202.70
M 3583 57101.00
S 2663 32386.75

Table 2

Hints:

  • gt_color_rows is useful for changing entire column colors
  • manual colors c("white", "skyblue")
  • Pastel1 palette from RColorBrewer package
Pizza Place Annual Pizza Sales
Fiscal Year 2015
number_sold profit
chicken
L 4932 102339.00
M 3894 65224.50
S 2224 28356.00
classic
L 4057 74518.50
M 4112 60581.75
S 6139 69870.25
veggie
L 5403 104202.70
M 3583 57101.00
S 2663 32386.75

Exercise 3

Using the house_illinois dataset derived from the house_price dataset, recreate the following graphic as precisely as possible.

Similar to the ggplot scales_*() function, the gt package has useful transformations that can be performed using the following syntax tab_*(), fmt_*(), cols_*(). Check the package documentation for a full list of options.

Code 
# data wrangling
house_illinois <- house_prices %>% 
  filter(state %in% c("Illinois")) %>% 
  mutate(year = year(date)) %>% 
  filter(year %in% c(2005:2010) ) %>% 
  group_by(year) %>% 
  summarize(
    hpi = mean(house_price_index),
    change = mean(house_price_perc)
    )

Hints:

  • color is "red" for all percents less than 0
  • percent is accounting
  • currency "USD"
  • "Housing Price Index" label is a tab_*() layer
Average Annual Housing Price Index (HPI)
Illinois Prices for 2006-2010
Housing Price Index
Value % Change
2005 $138.39 7.81%
2006 $145.69 5.31%
2007 $145.54 (0.09%)
1 2008 $135.06 (7.22%)
2009 $122.30 (9.40%)
2010 $116.27 (4.93%)
1 Housing market crash

Exercise 4

Adding icons and graphics to a table is made easy with gtExtras. Using the stream_data dataset, recreate the following graphic as precisely as possible.

Hints:

  • gt_plt_bullet()
  • gt_fa_column()
  • nytimes theme()
  • fmt_symbol_first()
distributor type ratio nominee
Netflix
WiFi
 27.7%
HBO
Video
 28.8 
NBC
Television
 20.5 
ABC
Television
 16.0 
Hulu
WiFi
 2.0 
FX Networks
Video
 11.1 
Prime Video
WiFi
 8.9 
Apple TV+
WiFi
 23.8 
CBS
Television
 2.5 

Exercise 5

Using the team_logo dataset, recreate the following graphic as precisely as possible.

The tab_options() layer for gt is similar to the theme() option for ggplot. Here you can add customization to the background colors, line type, size, etc.

Hints:

  • gt_img_rows with height = 30
  • tab options:
    • data row padding is px(1)
    • table background color is "gray90"
    • table width is 100%
    • table font color is "red"
    • column labels have background color "black", font size "large", and font weight "bold",
    • table body has hlines color "red"
    • table body has border bottom color "red"
Code 
# pipe the following code into your gt functions
team_logo %>%
  dplyr::select(logo = team_logo_espn, team_name, team_nick, team_conf) %>%
  slice_head(n = 6)
team_name team_nick team_conf
Arizona Cardinals Cardinals NFC
Atlanta Falcons Falcons NFC
Baltimore Ravens Ravens AFC
Buffalo Bills Bills AFC
Carolina Panthers Panthers NFC
Chicago Bears Bears NFC